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  {
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   "id": "ccafd318",
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   "outputs": [],
   "source": [
    "import hydromt\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from os.path import join, basename\n",
    "import glob\n",
    "import geopandas as gpd\n",
    "import pygeos\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "de90faae",
   "metadata": {},
   "outputs": [],
   "source": [
    "bbox =  32.0, -21.5, 35.5, -17.0\n",
    "root = r'/mnt/bazis/projects/hydromt-floodmodelling/00_data/'\n",
    "chunks={'stations': 100, 'time':-1}\n",
    "rm = {'station_x_coordinate':'lon', 'station_y_coordinate':'lat'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dda6e51e",
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       "    _FillValue:     nan</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 1440</li><li><span class='xr-has-index'>x</span>: 2880</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-96f808b4-edb9-4f3f-862e-47bbc014e62f' class='xr-array-in' type='checkbox' checked><label for='section-96f808b4-edb9-4f3f-862e-47bbc014e62f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1440, 2880), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-2a745502-1bb3-4d1d-b294-b0ebda3a43f8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2a745502-1bb3-4d1d-b294-b0ebda3a43f8' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.94 -89.81 ... 89.81 89.94</div><input id='attrs-c039ea34-6433-4624-89b0-3ef2a83bc18b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c039ea34-6433-4624-89b0-3ef2a83bc18b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6cde506-6207-40a1-87eb-0896ceb200d0' class='xr-var-data-in' type='checkbox'><label for='data-d6cde506-6207-40a1-87eb-0896ceb200d0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-89.9375, -89.8125, -89.6875, ...,  89.6875,  89.8125,  89.9375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.9 -179.8 ... 179.8 179.9</div><input id='attrs-913ac53b-d554-469f-a4a9-ed387609d69e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-913ac53b-d554-469f-a4a9-ed387609d69e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff4e77dc-2890-4f99-a6fc-65624f019992' class='xr-var-data-in' type='checkbox'><label for='data-ff4e77dc-2890-4f99-a6fc-65624f019992' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-179.9375, -179.8125, -179.6875, ...,  179.6875,  179.8125,  179.9375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-d23e631c-0559-446e-9e2f-10a3080d6e48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d23e631c-0559-446e-9e2f-10a3080d6e48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f781e3d9-a72f-45af-a9a8-888dd906a7dc' class='xr-var-data-in' type='checkbox'><label for='data-f781e3d9-a72f-45af-a9a8-888dd906a7dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AXIS[&quot;Latitude&quot;,NORTH],AXIS[&quot;Longitude&quot;,EAST],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]]</dd></dl></div><div class='xr-var-data'><pre>array(1)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-85bf867a-5f07-4000-b59f-7ff39bf271e6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-85bf867a-5f07-4000-b59f-7ff39bf271e6' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>_FillValue :</span></dt><dd>nan</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (y: 1440, x: 2880)>\n",
       "dask.array<getitem, shape=(1440, 2880), dtype=float64, chunksize=(1440, 2880), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * y            (y) float64 -89.94 -89.81 -89.69 -89.56 ... 89.69 89.81 89.94\n",
       "  * x            (x) float64 -179.9 -179.8 -179.7 -179.6 ... 179.7 179.8 179.9\n",
       "    spatial_ref  int64 1\n",
       "Attributes:\n",
       "    AREA_OR_POINT:  Area\n",
       "    _FillValue:     nan"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## read AVISO MDT\n",
    "mdt_fn = r'/mnt/bazis_data/hydrology/topography/mdt/aviso/MDT_CNES_CLS18_global_filled.tif'\n",
    "da_mdt = hydromt.open_raster(mdt_fn)\n",
    "da_mdt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "137537b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "## read CMF pits\n",
    "mapdir = r'/mnt/bazis/projects/hydromt-floodmodelling/02_models/cmf/map/beira_06min'\n",
    "gdf_pits = gpd.read_file(join(mapdir, 'gis', 'pits.geojson'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "867c4ce6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def cmf_pit_mapping(gdf_gtsm, name, gdf_pits=gdf_pits, mapdir=mapdir, max_dist=10e3):\n",
    "    pts = pygeos.points([g.coords[:][0] for g in gdf_pits.geometry])\n",
    "    gtsm_idx = gdf_gtsm.sindex.nearest(pts)[1]\n",
    "    gdf_pits['gtsm_idx'] = gtsm_idx + 1\n",
    "    gdf_pits['gtsm_dst']  = gdf_gtsm.iloc[gtsm_idx].to_crs(32736).distance(gdf_pits.to_crs(32736), align=False).values\n",
    "    gdf_pits = gdf_pits[gdf_pits['gtsm_dst']<max_dist]\n",
    "    nlinks = gdf_pits.index.size\n",
    "    print(nlinks)\n",
    "    cmf_gtsm_ref = gdf_pits[['col', 'row', 'gtsm_idx']].fillna(0).values.astype(np.int32)\n",
    "    with open(join(mapdir, f'{name}.txt'), 'w') as f:\n",
    "        f.write(f'{nlinks:d}\\n')\n",
    "        np.savetxt(\n",
    "            f, \n",
    "            cmf_gtsm_ref,\n",
    "            fmt='%3.d',\n",
    "        )\n",
    "    return gdf_pits, cmf_gtsm_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "64b3ced2",
   "metadata": {},
   "outputs": [
    {
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       "      <td>POINT (35.40500 -19.43800)</td>\n",
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      "text/plain": [
       "                            geometry\n",
       "stations                            \n",
       "18278     POINT (34.76100 -20.55200)\n",
       "17492     POINT (34.87800 -19.84900)\n",
       "17493     POINT (34.76100 -19.99500)\n",
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       "17495     POINT (34.70200 -20.40500)\n",
       "12318     POINT (35.08300 -21.13800)\n",
       "34418     POINT (34.99500 -19.99500)\n",
       "17491     POINT (35.08300 -19.73100)\n",
       "17490     POINT (35.25900 -19.58500)\n",
       "17489     POINT (35.40500 -19.43800)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## CODEC\n",
    "name = 'GTSM_CODEC'\n",
    "fns = glob.glob(join(root, name, 'reanalysis_waterlevel_10min_*_v1_beira.nc'))\n",
    "ds_gtsm0 = xr.open_dataset(fns[0], chunks=chunks).rename(rm).vector.clip_bbox(bbox)\n",
    "gdf_gtsm = ds_gtsm0.vector.to_gdf().set_crs(4326)\n",
    "gdf_gtsm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c3aba3b8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[35, 23, 15],\n",
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       "       [31, 44,  6]], dtype=int32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf_pits1, cmf_gtsm_ref = cmf_pit_mapping(gdf_gtsm, name)\n",
    "fig, ax = plt.subplots(1,1, figsize=(6,6))\n",
    "gdf_pits.plot(markersize=(gdf_pits['upa_10'].values-7)*7, color='b', ax=ax)\n",
    "gdf_pits1.plot(markersize=(gdf_pits1['upa_10'].values-7)*7, color='k', ax=ax)\n",
    "gdf_gtsm.iloc[np.unique(gdf_pits1['gtsm_idx'].values-1)].plot(markersize=15, color='r', marker='^', ax=ax)\n",
    "cmf_gtsm_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0904a026",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reanalysis_waterlevel_10min_2017_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2018_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1981_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1991_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2008_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2007_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2005_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1993_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1983_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2015_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1985_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2013_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2003_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1995_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1997_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2001_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1998_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1988_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2011_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1987_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1994_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2002_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2012_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1984_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1986_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2010_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1989_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1999_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2000_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1996_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2006_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2009_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1990_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1980_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2016_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1979_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2014_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1982_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_1992_v1_beira.nc\n",
      "reanalysis_waterlevel_10min_2004_v1_beira.nc\n"
     ]
    }
   ],
   "source": [
    "mdt0 = da_mdt.raster.sample(gdf_gtsm).rename({'index': 'stations'}).reset_coords(drop=True)\n",
    "for fn in fns: \n",
    "    print(basename(fn))\n",
    "    fn_out = fn.replace('.nc', '_egm.nc')\n",
    "    ds_gtsm = xr.open_dataset(fn, chunks=chunks).sel(stations=gdf_gtsm.index.values)\n",
    "    ds_gtsm = ds_gtsm + mdt0\n",
    "    encoding = {'waterlevel': {'dtype': 'float32'}}\n",
    "    ds_gtsm.to_netcdf(fn_out, encoding=encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f2fddd99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([b'id_coast_glob_12352', b'id_coast_glob_12353',\n",
       "       b'id_coast_glob_17536', b'id_coast_glob_17537',\n",
       "       b'id_coast_glob_17538', b'id_coast_glob_17539',\n",
       "       b'id_coast_glob_17540', b'id_coast_glob_17541',\n",
       "       b'id_coast_glob_17542', b'id_coast_glob_18328',\n",
       "       b'id_coast_glob_18329', b'id_coast_glob_18331',\n",
       "       b'id_coast_glob_18333', b'id_coast_glob_18335',\n",
       "       b'id_reg_grid_glob_01456'], dtype='|S64')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## IDAI / ELOISE\n",
    "name = 'GTSM_20190218_20190401_IDAI'\n",
    "tstart, tstop = '2019-01-01', '2019-05-01'\n",
    "prefix = 'idai'\n",
    "\n",
    "name = 'GTSM_20201218_20210201_ELOISE'\n",
    "tstart, tstop = '2021-01-01', '2021-03-01'\n",
    "prefix = 'eloise'\n",
    "\n",
    "var = 'waterlevel'\n",
    "runs = ['era5', 'spw', 'tides', 'era5_tides', 'spw_tides', 'era5_spw_tides']\n",
    "fn0 = join(root, name, f'{prefix}_{runs[0]}_his.nc')\n",
    "da_gtsm0 = xr.open_dataset(fn0, chunks=chunks).rename(rm)[var]\n",
    "da_gtsm0['stations'] = da_gtsm0.stations\n",
    "da_gtsm0 = da_gtsm0.vector.clip_bbox(bbox)\n",
    "\n",
    "da_gtsm0.raster.set_crs(4326)\n",
    "gdf_gtsm = da_gtsm0.vector.to_gdf()\n",
    "mdt0 = da_mdt.raster.sample(gdf_gtsm).rename({'index': 'stations'}).reset_coords(drop=True)\n",
    "da_gtsm0.station_name.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fa11da09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[35, 23,  3],\n",
       "       [33, 24,  3],\n",
       "       [34, 25,  3],\n",
       "       [35, 25,  3],\n",
       "       [32, 27,  5],\n",
       "       [33, 27,  4],\n",
       "       [31, 28,  5],\n",
       "       [27, 29, 11],\n",
       "       [28, 29, 11],\n",
       "       [29, 29, 10],\n",
       "       [27, 31,  8],\n",
       "       [28, 32,  8],\n",
       "       [28, 33,  8],\n",
       "       [27, 34,  9],\n",
       "       [27, 36, 12],\n",
       "       [28, 36, 12],\n",
       "       [30, 37,  1],\n",
       "       [30, 38,  1],\n",
       "       [31, 38,  1],\n",
       "       [31, 40, 13],\n",
       "       [31, 41,  2],\n",
       "       [30, 42,  2],\n",
       "       [31, 43, 14],\n",
       "       [30, 44, 14],\n",
       "       [31, 44, 14]], dtype=int32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf_pits1, cmf_gtsm_ref = cmf_pit_mapping(gdf_gtsm, name)\n",
    "fig, ax = plt.subplots(1,1, figsize=(6,6))\n",
    "gdf_pits.plot(markersize=(gdf_pits['upa_10'].values-7)*7, color='b', ax=ax)\n",
    "gdf_pits1.plot(markersize=(gdf_pits1['upa_10'].values-7)*7, color='k', ax=ax)\n",
    "gdf_gtsm.iloc[np.unique(gdf_pits1['gtsm_idx'].values-1)].plot(markersize=15, color='r', marker='^', ax=ax)\n",
    "cmf_gtsm_ref"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "d7a61c3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "for run in runs:\n",
    "    fn = join(root, name, f'{prefix}_{run}_his.nc')\n",
    "    fn_out = fn.replace('.nc', '_beira.nc')\n",
    "\n",
    "    # READ\n",
    "    da_gtsm = xr.open_dataset(fn, chunks=chunks)[var].sel(stations=gdf_gtsm.index)\n",
    "\n",
    "    # reindex to get timeseries from JAN-01\n",
    "    time = xr.IndexVariable('time', pd.date_range(tstart, tstop, freq='10MIN'))\n",
    "    da_gtsm = da_gtsm.resample(time='10MIN').nearest().reindex(time=time, fill_value=0)\n",
    "\n",
    "    # apply MDT\n",
    "    if 'tides' in run:\n",
    "        da_gtsm = da_gtsm + mdt0\n",
    "        fn_out = fn_out.replace('.nc', '_egm.nc')\n",
    "\n",
    "    # write to nc\n",
    "    da_gtsm.name = var\n",
    "    encoding = {var: {'dtype': 'float32'}}\n",
    "    da_gtsm.to_netcdf(fn_out, encoding=encoding)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "c6e50ef4",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;waterlevel&#x27; (time: 17281, stations: 15)&gt;\n",
       "dask.array&lt;add, shape=(17281, 15), dtype=float64, chunksize=(17281, 7), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * stations              (stations) int64 12317 12318 17489 ... 18282 34418\n",
       "  * time                  (time) datetime64[ns] 2019-01-01 ... 2019-05-01\n",
       "    station_x_coordinate  (stations) float64 dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;\n",
       "    station_y_coordinate  (stations) float64 dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;\n",
       "    station_name          (stations) |S64 dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'waterlevel'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 17281</li><li><span class='xr-has-index'>stations</span>: 15</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7130938c-5477-4e80-bbd4-a194a899e486' class='xr-array-in' type='checkbox' checked><label for='section-7130938c-5477-4e80-bbd4-a194a899e486' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(17281, 2), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "    <tr><th> Bytes </th><td> 1.98 MiB </td> <td> 0.92 MiB </td></tr>\n",
       "    <tr><th> Shape </th><td> (17281, 15) </td> <td> (17281, 7) </td></tr>\n",
       "    <tr><th> Count </th><td> 489 Tasks </td><td> 4 Chunks </td></tr>\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >15</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">17281</text>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-21fd0adf-f582-4b06-9b94-a822cd81a58e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-21fd0adf-f582-4b06-9b94-a822cd81a58e' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>stations</span></div><div class='xr-var-dims'>(stations)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>12317 12318 17489 ... 18282 34418</div><input id='attrs-30ed1da0-86f2-44ed-a12d-c5f42463e83a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-30ed1da0-86f2-44ed-a12d-c5f42463e83a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93f284d8-a2f6-4883-9598-0ca983ace11e' class='xr-var-data-in' type='checkbox'><label for='data-93f284d8-a2f6-4883-9598-0ca983ace11e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([12317, 12318, 17489, 17490, 17491, 17492, 17493, 17494, 17495, 18275,\n",
       "       18276, 18278, 18280, 18282, 34418])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2019-01-01 ... 2019-05-01</div><input id='attrs-e58160c5-5e05-4fe7-8eb4-9d87da45f02a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e58160c5-5e05-4fe7-8eb4-9d87da45f02a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4844612b-dba0-45db-a9d4-84e63a25fe83' class='xr-var-data-in' type='checkbox'><label for='data-4844612b-dba0-45db-a9d4-84e63a25fe83' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2019-01-01T00:10:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T00:20:00.000000000&#x27;, ..., &#x27;2019-04-30T23:40:00.000000000&#x27;,\n",
       "       &#x27;2019-04-30T23:50:00.000000000&#x27;, &#x27;2019-05-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>station_x_coordinate</span></div><div class='xr-var-dims'>(stations)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2,), meta=np.ndarray&gt;</div><input id='attrs-1eb67a1c-8d66-46c5-80cc-fe103533e299' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1eb67a1c-8d66-46c5-80cc-fe103533e299' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3fa5cd3-850c-4c11-8ced-deae71a4601c' class='xr-var-data-in' type='checkbox'><label for='data-a3fa5cd3-850c-4c11-8ced-deae71a4601c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>original x-coordinate of station (non-snapped)</dd></dl></div><div class='xr-var-data'><table>\n",
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      ],
      "text/plain": [
       "<xarray.DataArray 'waterlevel' (time: 17281, stations: 15)>\n",
       "dask.array<add, shape=(17281, 15), dtype=float64, chunksize=(17281, 7), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * stations              (stations) int64 12317 12318 17489 ... 18282 34418\n",
       "  * time                  (time) datetime64[ns] 2019-01-01 ... 2019-05-01\n",
       "    station_x_coordinate  (stations) float64 dask.array<chunksize=(2,), meta=np.ndarray>\n",
       "    station_y_coordinate  (stations) float64 dask.array<chunksize=(2,), meta=np.ndarray>\n",
       "    station_name          (stations) |S64 dask.array<chunksize=(2,), meta=np.ndarray>"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "da_gtsm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "958c3c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca24d2f040>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24d2f310>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24d2f160>,\n",
       " <matplotlib.lines.Line2D at 0x7fca25f09220>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24d7b430>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24e929a0>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24bde910>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24bde670>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24bde970>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24bdef10>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24d84ac0>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24bdedf0>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24bdeee0>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24bdebb0>,\n",
       " <matplotlib.lines.Line2D at 0x7fca24bde610>]"
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     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "da_gtsm.sel(time=slice('2021-01-18', '2021-01-25')).plot.line(x='time')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
